Chapter 4 Community composition

load("data/data.Rdata")

4.1 Taxonomy overview

4.1.1 Stacked barplot

genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  filter(count > 0) %>% #filter 0 counts
  #mutate(individual=factor(individual,levels=c("Sg1","Sg2","Sg3","Sg4","Sg5","Sg6","Sg7","Sg8","Sg9","Sg10"))) %>%
  ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
    facet_nested(. ~ type,  scales="free") + #facet per day and treatment
    guides(fill = guide_legend(ncol = 1)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
   labs(fill="Phylum",y = "Relative abundance",x="Samples")

4.1.2 Phylum relative abundances

phylum_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count))

phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

phylum	mean	sd
p__Bacteroidota	0.376352935	0.205078492
p__Bacillota_A	0.250845368	0.165219604
p__Bacillota	0.116698680	0.146369286
p__Pseudomonadota	0.095767942	0.159976047
p__Campylobacterota	0.052429876	0.092626270
p__Verrucomicrobiota	0.029518627	0.072768229
p__Desulfobacterota	0.023304680	0.036356268
p__Actinomycetota	0.012917195	0.107880476
p__Chlamydiota	0.010449046	0.059349062
p__Fusobacteriota	0.010359534	0.028167393
p__Cyanobacteriota	0.009098787	0.016424861
p__Bacillota_C	0.004658039	0.006626014
p__Spirochaetota	0.003962784	0.012243051
p__Bacillota_B	0.002436629	0.004905803
p__Elusimicrobiota	0.001199879	0.006464719

phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    filter(phylum %in% phylum_arrange) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        theme(legend.position="none") +
        labs(y="Phylum",x="Relative abundance")

4.2 Taxonomy boxplot

4.2.1 Family

family_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family) %>%
  summarise(relabun=sum(count))

family_summary %>%
    group_by(family) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

family	mean	sd
f__Bacteroidaceae	2.189238e-01	0.1404298747
f__Lachnospiraceae	1.389745e-01	0.1089608538
f__Tannerellaceae	1.016053e-01	0.0802366634
f__Helicobacteraceae	5.197482e-02	0.0922005028
f__Mycoplasmoidaceae	3.651280e-02	0.0753015455
f__Erysipelotrichaceae	3.461428e-02	0.0450141949
f__UBA3700	3.378612e-02	0.0555421417
f__Marinifilaceae	2.741603e-02	0.0272043836
f__	2.740335e-02	0.0833966811
f__DTU072	2.668734e-02	0.0526713763
f__Rikenellaceae	2.665357e-02	0.0462652505
f__Enterobacteriaceae	2.660196e-02	0.0913429878
f__Coprobacillaceae	2.521928e-02	0.0887342893
f__Desulfovibrionaceae	2.330468e-02	0.0363562684
f__Ruminococcaceae	1.838184e-02	0.0427420605
f__LL51	1.738859e-02	0.0683714736
f__Rhizobiaceae	1.578130e-02	0.0766280394
f__UBA3830	1.560351e-02	0.0451659123
f__Mycobacteriaceae	1.228390e-02	0.1079320546
f__Akkermansiaceae	1.213003e-02	0.0315247341
f__Chlamydiaceae	1.044905e-02	0.0593490616
f__Fusobacteriaceae	1.035953e-02	0.0281673926
f__CAG-239	9.034415e-03	0.0150424841
f__Enterococcaceae	8.322037e-03	0.0463917038
f__Gastranaerophilaceae	7.638375e-03	0.0143794214
f__Oscillospiraceae	6.565519e-03	0.0077974910
f__UBA1997	6.303806e-03	0.0307918484
f__Streptococcaceae	6.291980e-03	0.0340225424
f__UBA1242	4.618700e-03	0.0152906152
f__Brevinemataceae	3.962784e-03	0.0122430505
f__Acutalibacteraceae	3.335081e-03	0.0108974502
f__UBA660	3.159125e-03	0.0138778160
f__Clostridiaceae	2.808963e-03	0.0170355922
f__RUG11792	2.784774e-03	0.0249664653
f__Peptococcaceae	2.436629e-03	0.0049058034
f__MGBC116941	2.134904e-03	0.0093302936
f__Acidaminococcaceae	1.921070e-03	0.0050068934
f__CAG-508	1.786832e-03	0.0063826540
f__Anaerovoracaceae	1.551174e-03	0.0036295746
f__Moraxellaceae	1.468042e-03	0.0096884882
f__RUG14156	1.460412e-03	0.0045605184
f__Staphylococcaceae	1.345783e-03	0.0050584305
f__Elusimicrobiaceae	1.199879e-03	0.0064647185
f__CAG-288	9.379861e-04	0.0059852367
f__Anaerotignaceae	8.884602e-04	0.0040242420
f__CALVMC01	7.428930e-04	0.0043360033
f__Eggerthellaceae	6.332937e-04	0.0021152288
f__Massilibacillaceae	6.162148e-04	0.0016267915
f__UBA1820	4.680638e-04	0.0012990360
f__Arcobacteraceae	4.550604e-04	0.0050029784
f__CAG-274	4.466884e-04	0.0021903937
f__Burkholderiaceae_C	3.656162e-04	0.0047810524
f__Muribaculaceae	3.575580e-04	0.0009726557
f__UBA932	3.379555e-04	0.0011520742
f__Hepatoplasmataceae	2.954146e-04	0.0038630477
f__Rhodobacteraceae	2.924483e-04	0.0038242573
f__Weeksellaceae	2.739210e-04	0.0031292133
f__Eubacteriaceae	1.627561e-04	0.0006691724
f__Sphingobacteriaceae	1.488164e-04	0.0012387571
f__Devosiaceae	1.472568e-04	0.0015006126
f__Pumilibacteraceae	1.262477e-04	0.0007602888
f__WRAU01	9.491039e-05	0.0012411144
f__Peptostreptococcaceae	2.260586e-05	0.0002956099

family_arrange <- family_summary %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    pull()

family_summary %>%
    left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    left_join(sample_metadata,by=join_by(sample==Tube_code)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~type)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

4.2.2 Genus

genus_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>% #append sample metadata
  left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,genus) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__")

genus_summary %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun, na.rm=TRUE),sd=sd(relabun, na.rm=TRUE)) %>%
    arrange(-mean) %>%
    tt()

genus	mean	sd
g__Bacteroides	1.337013e-01	0.0929327655
g__Parabacteroides	9.568074e-02	0.0803866834
g__Phocaeicola	6.854013e-02	0.0793558268
g__Mycoplasmoides	3.015602e-02	0.0749534641
g__Helicobacter_J	2.973811e-02	0.0592188466
g__Odoribacter	2.522456e-02	0.0267412147
g__Roseburia	2.356827e-02	0.0556733585
g__NHYM01	2.223671e-02	0.0792620634
g__Alistipes	2.182523e-02	0.0282922451
g__Coprobacillus	1.990305e-02	0.0873936500
g__Agrobacterium	1.578130e-02	0.0766280394
g__Corynebacterium	1.228390e-02	0.1079320546
g__Akkermansia	1.213003e-02	0.0315247341
g__Fusobacterium_A	1.026752e-02	0.0281722895
g__Kineothrix	8.698172e-03	0.0406735379
g__Proteus	8.556721e-03	0.0677872967
g__Dielma	8.477779e-03	0.0090773537
g__CAG-95	8.014504e-03	0.0203997532
g__JAAYNV01	7.901098e-03	0.0194443338
g__UBA866	7.175409e-03	0.0291873132
g__Desulfovibrio	6.936071e-03	0.0210368286
g__Enterococcus	6.919414e-03	0.0453969341
g__Ureaplasma	6.356785e-03	0.0137373135
g__Lactococcus	6.291980e-03	0.0340225424
g__Lacrimispora	5.993828e-03	0.0097829951
g__Parabacteroides_B	5.924566e-03	0.0099792665
g__CALXRO01	5.698293e-03	0.0306767185
g__Citrobacter	5.650169e-03	0.0332631355
g__NSJ-61	5.495080e-03	0.0197757124
g__Breznakia	5.440147e-03	0.0235692957
g__Clostridium_AQ	5.263895e-03	0.0121113284
g__Salmonella	5.073541e-03	0.0183551010
g__Bilophila	4.925550e-03	0.0088098210
g__Hungatella_A	4.755524e-03	0.0095127458
g__MGBC136627	4.313746e-03	0.0162110012
g__Escherichia	4.139378e-03	0.0264569200
g__UMGS1251	4.111189e-03	0.0072426766
g__Hungatella	4.068243e-03	0.0189714607
g__Clostridium_Q	3.965072e-03	0.0052000165
g__Brevinema	3.962784e-03	0.0122430505
g__Thomasclavelia	3.856935e-03	0.0108480346
g__Scatousia	3.607252e-03	0.0102197530
g__Enterocloster	3.578040e-03	0.0047221634
g__Mailhella	3.569833e-03	0.0101940649
g__Copromonas	3.557270e-03	0.0050035176
g__Ventrimonas	3.472264e-03	0.0070914148
g__Caccovivens	3.304213e-03	0.0121527504
g__Lawsonia	3.250547e-03	0.0116543597
g__Fournierella	3.179598e-03	0.0062039376
g__Limenecus	3.126281e-03	0.0065432982
g__MGBC133411	3.006834e-03	0.0074209199
g__Mucinivorans	2.866176e-03	0.0371005376
g__Sarcina	2.808963e-03	0.0170355922
g__Acetatifactor	2.706113e-03	0.0058179611
g__Eisenbergiella	2.665864e-03	0.0067990748
g__Bacteroides_G	2.651345e-03	0.0344120334
g__CAJLXD01	2.603014e-03	0.0087025932
g__Blautia	2.528781e-03	0.0061107674
g__C-19	2.248901e-03	0.0048466847
g__Butyricimonas	2.191467e-03	0.0049842986
g__Velocimicrobium	2.175479e-03	0.0066412559
g__CAZU01	2.170696e-03	0.0065598332
g__MGBC116941	2.134904e-03	0.0093302936
g__Intestinimonas	2.057548e-03	0.0039214573
g__Negativibacillus	2.044877e-03	0.0054857591
g__Rikenella	1.962168e-03	0.0036910712
g__Phascolarctobacterium	1.921070e-03	0.0050068934
g__RGIG6463	1.768909e-03	0.0039495868
g__JALFVM01	1.721982e-03	0.0038441918
g__Oscillibacter	1.492792e-03	0.0024898538
g__Pseudoflavonifractor	1.485397e-03	0.0027590442
g__Acinetobacter	1.468042e-03	0.0096884882
g__Citrobacter_A	1.376632e-03	0.0060011629
g__Staphylococcus	1.345783e-03	0.0050584305
g__RGIG4733	1.288541e-03	0.0040266511
g__UBA1436	1.199879e-03	0.0064647185
g__Lachnotalea	1.194066e-03	0.0048896855
g__Ruthenibacterium	1.187962e-03	0.0053331515
g__14-2	1.170787e-03	0.0095739735
g__Beduini	1.160220e-03	0.0025025910
g__Scatocola	1.108079e-03	0.0044726561
g__Faecisoma	1.072888e-03	0.0055281036
g__Enterococcus_A	1.071313e-03	0.0098572488
g__Faecimonas	9.764964e-04	0.0082595968
g__RGIG9287	9.526092e-04	0.0092684203
g__CAG-345	9.379861e-04	0.0059852367
g__Blautia_A	9.100332e-04	0.0028927770
g__RGIG1896	8.246024e-04	0.0062408705
g__CAG-269	7.889086e-04	0.0046906408
g__Marseille-P3106	7.845423e-04	0.0017535790
g__WRHT01	6.354364e-04	0.0026829604
g__Eggerthella	6.332937e-04	0.0021152288
g__IOR16	6.324101e-04	0.0020540977
g__Anaerotruncus	6.192165e-04	0.0016862064
g__RUG14156	6.079829e-04	0.0022026464
g__CHH4-2	6.073170e-04	0.0019890673
g__Serratia_A	5.792071e-04	0.0075741155
g__CAG-56	4.857941e-04	0.0016254203
g__Merdimorpha	4.680638e-04	0.0012990360
g__MGBC140009	4.624605e-04	0.0023930648
g__CALURL01	4.581073e-04	0.0016646244
g__Aliarcobacter	4.550604e-04	0.0050029784
g__Scatenecus	4.467347e-04	0.0019650337
g__RGIG8482	4.347613e-04	0.0029582243
g__Enterobacter	4.025794e-04	0.0041076314
g__Klebsiella	4.007019e-04	0.0048624260
g__Caccenecus	3.895102e-04	0.0017702443
g__Alcaligenes	3.656162e-04	0.0047810524
g__Plesiomonas	3.590755e-04	0.0026947991
g__HGM05232	3.575580e-04	0.0009726557
g__JAHHSE01	3.550107e-04	0.0014818046
g__Egerieousia	3.379555e-04	0.0011520742
g__Emergencia	3.373704e-04	0.0017351293
g__Enterococcus_B	3.313107e-04	0.0022138492
g__Stoquefichus	2.990680e-04	0.0020385612
g__Hepatoplasma	2.954146e-04	0.0038630477
g__Paracoccus	2.924483e-04	0.0038242573
g__Moheibacter	2.739210e-04	0.0031292133
g__Scatomorpha	2.610126e-04	0.0010128258
g__UBA7185	2.405856e-04	0.0014474682
g__Eubacterium	1.627561e-04	0.0006691724
g__Sphingobacterium	1.488164e-04	0.0012387571
g__Devosia	1.472568e-04	0.0015006126
g__Anaerosporobacter	1.437105e-04	0.0012673026
g__Caccomorpha	1.366945e-04	0.0010479478
g__UBA2658	1.292159e-04	0.0007163845
g__Protoclostridium	1.262477e-04	0.0007602888
g__Angelakisella	1.253643e-04	0.0009168117
g__Cetobacterium_A	9.201325e-05	0.0008667719
g__Rahnella	6.395024e-05	0.0008362579
g__Peptostreptococcus	2.260586e-05	0.0002956099

genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary %>%
    left_join(genome_metadata %>% select(genus,phylum) %>% unique(),by=join_by(genus==genus)) %>%
    left_join(sample_metadata,by=join_by(sample==Tube_code)) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    filter(genus %in% genus_arrange[1:20]) %>%
    mutate(genus=factor(genus,levels=rev(genus_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
        scale_color_manual(values=phylum_colors[-c(3,4,6,8)]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~type)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

4.3 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
  to.elements(., GIFT_db) %>%
  traits2dist(., method = "gower")

functional <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, dist = dist) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(functional = 1) %>%
  rownames_to_column(var = "sample") %>%
  mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample)) %>%
  full_join(functional, by = join_by(sample == sample))

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = species, group=species, color=species, fill=species)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="species",
          breaks=c("cloaca","feces"),
          labels=c("Cloaca","Faeces"),
          values=c("#e5bd5b", "#6b7398")) +
      scale_fill_manual(name="Sample type",
          breaks=c("cloaca","feces"),
          labels=c("Cloaca","Faeces"),
          values=c("#e5bd5b50", "#6b739850")) +
      facet_wrap(. ~ metric, scales = "free", ncol=4) +
      coord_cartesian(xlim = c(1, NA)) +
      theme_classic() +
      theme(
        strip.background = element_blank(),
        panel.grid.minor.x = element_line(size = .1, color = "grey"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)
      )

4.4 Beta diversity

beta_q0n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

beta_q1f <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, dist = dist)

4.5 Permanovas

4.5.0.1 Load required data

sample_metadata<-sample_metadata  %>%
  filter(Tube_code != "AD35")

meta <- column_to_rownames(sample_metadata, "Tube_code")

4.5.1 1. Are the wild populations similar?

4.5.1.1 Wild: P.muralis vs P.liolepis

4.5.1.2 Number of samples used

[1] 28

beta_div_richness_wild<-hillpair(data=wild.counts, q=0)
beta_div_neutral_wild<-hillpair(data=wild.counts, q=1)
beta_div_phylo_wild<-hillpair(data=wild.counts, q=1, tree=genome_tree)
beta_div_func_wild<-hillpair(data=wild.counts_func, q=1, dist=dist)

4.5.1.2.1 Neutral

	Df	SumOfSqs	R2	F	Pr(>F)
species	1	2.018797	0.2566342	8.976049	0.001
Residual	26	5.847641	0.7433658	NA	NA
Total	27	7.866438	1.0000000	NA	NA

4.5.1.2.2 Phylogenetic

	Df	SumOfSqs	R2	F	Pr(>F)
species	1	0.3786052	0.2108638	6.947419	0.001
Residual	26	1.4168908	0.7891362	NA	NA
Total	27	1.7954960	1.0000000	NA	NA

4.5.1.2.3 Functional

	Df	SumOfSqs	R2	F	Pr(>F)
species	1	0.0787916	0.1594096	4.930642	0.074
Residual	26	0.4154800	0.8405904	NA	NA
Total	27	0.4942716	1.0000000	NA	NA

There is a significant difference between P.muralis and P.liolepis regarding their neutral and phylogenetic beta diversity but not in their functional part. However, their is a big part of the diversity over 70-80% that isn’t captured in the model. Maybe due to the difference at the individual level.

Warning in get_plot_component(plot, "guide-box"): Multiple components found; returning the first one. To return all, use `return_all =
TRUE`.

Warning in get_plot_component(plot, "guide-box"): Multiple components found; returning the first one. To return all, use `return_all =
TRUE`.

There are some differences regarding neutral and phylogenetic beta diversity when comparing the 0_Wild samples between both species.

4.5.2 2. Do the antibiotics work?

4.5.2.1 Acclimation vs antibiotics

4.5.2.2 Number of samples used

[1] 55

beta_div_neutral_treat<-hillpair(data=treat.counts, q=1)
beta_div_phylo_treat<-hillpair(data=treat.counts, q=1, tree=genome_tree)
beta_div_func_treat<-hillpair(data=treat.counts_func, q=1, dist=dist)

4.5.2.2.1 Neutral

	Df	SumOfSqs	R2	F	Pr(>F)
time_point	1	2.189257	0.1017464	8.979451	0.001
species	1	3.046568	0.1415902	12.495800	0.001
individual	26	9.941985	0.4620568	1.568386	0.001
Residual	26	6.338992	0.2946066	NA	NA
Total	54	21.516802	1.0000000	NA	NA

4.5.2.2.2 Phylogenetic

	Df	SumOfSqs	R2	F	Pr(>F)
time_point	1	2.1642230	0.2000313	18.460841	0.001
species	1	0.8686306	0.0802844	7.409427	0.001
individual	26	4.7385071	0.4379630	1.554596	0.005
Residual	26	3.0480626	0.2817214	NA	NA
Total	54	10.8194233	1.0000000	NA	NA

4.5.2.2.3 Functional

	Df	SumOfSqs	R2	F	Pr(>F)
time_point	1	2.2832796	0.3550091	33.1502018	0.001
species	1	0.0227598	0.0035387	0.3304425	0.703
individual	26	2.3347730	0.3630154	1.3037622	0.217
Residual	26	1.7907967	0.2784368	NA	NA
Total	54	6.4316092	1.0000000	NA	NA

Time_point is significantly different in all the 3 diversity metrics and the individual factor also in the neutral diversity metric.

Warning in get_plot_component(plot, "guide-box"): Multiple components found; returning the first one. To return all, use `return_all =
TRUE`.